Purpose:

The Python script identifies and filters paused CCT groups based on a specific date range.

To Elaborate

The Python script processes data related to CCT groups, specifically focusing on those that have been paused. It extracts and analyzes the pause date and reason from a given data source. The script’s primary function is to filter out CCT groups that have been paused within the last five days. This is achieved by comparing the extracted pause dates against a calculated date, which is five days prior to the current date. The filtered results are then prepared for further analysis or reporting, ensuring that only relevant paused groups within the specified timeframe are considered.

Walking Through the Code

1. Data Preparation:
   • The script begins by defining the primary data source and relevant columns from the input data. This includes columns for group, account, campaign, and other identifiers.
2. Data Extraction:
   • It extracts the ‘Pause Date’ and ‘Pause Reason’ from the ‘CCT_Group_pause_check’ column by splitting the string data. This is crucial for identifying when and why a group was paused.
3. Date Conversion:
   • The extracted ‘Pause Date’ strings are converted into datetime objects to facilitate date comparisons.
4. Date Calculation:
   • The script calculates a date five days prior to the current date. This is used as a threshold to filter the data.
5. Data Filtering:
   • The script filters the DataFrame to include only those entries where the ‘Pause Date’ is within the last five days. This results in a subset of data that meets the specified criteria.
6. Output Preparation:
   • The filtered data is assigned to outputDf, ready for further use or analysis.

Vitals

• Script ID : 627
• Client ID / Customer ID: 1306924347 / 69058
• Action Type: Email Report
• Item Changed: None
• Output Columns:
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: Jonathan Reichl (jreichl@marinsoftware.com)
• Created by Jonathan Reichl on 2023-12-22 16:15
• Last Updated by Jonathan Reichl on 2023-12-22 16:16

> See it in Action

Python Code

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##
## name: alert_cct_group_pasued
## description:
##  
## 
## author: 
## created: 2023-12-19
## 

today = datetime.datetime.now(CLIENT_TIMEZONE).date()

# primary data source and columns
inputDf = dataSourceDict["1"]
RPT_COL_GROUP = 'Group'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_CCT_SOURCETEMPLATE = 'CCT_sourcetemplate'
RPT_COL_PUB_ID = 'Pub. ID'
RPT_COL_STUDIO = 'Studio'
RPT_COL_CCT_SOURCETEMPLATE_BACKUP = 'CCT_SourceTemplate_Backup'
RPT_COL_CCT_GROUP_PAUSE_CHECK = 'CCT_Group_pause_check'

# output columns and initial values

# user code start here
print(tableize(inputDf))



# Splitting and extracting date
inputDf['Pause Date'] = inputDf[RPT_COL_CCT_GROUP_PAUSE_CHECK].str.split('|').str[2]

inputDf['Pause Reason'] = inputDf[RPT_COL_CCT_GROUP_PAUSE_CHECK].str.split('|').str[1]


# Convert strings to datetime objects
inputDf['Pause Date'] = pd.to_datetime(inputDf['Pause Date'])


# Calculating 5 days ago from today
five_days_ago = today - datetime.timedelta(days=5)

# Convert the 'five_days_ago' date object to a pandas Timestamp for comparison
five_days_ago = pd.Timestamp(five_days_ago)



# Filtering the DataFrame
filtered_df = inputDf[inputDf['Pause Date'] >= five_days_ago]

outputDf = filtered_df

Post generated on 2025-03-11 01:25:51 GMT